Science-Based Policy Recommendations for Managing Emerging Pollutants: Protecting Water Quality for the Health of People and the Environment
Bibliographic record
Abstract
Emerging water pollutants are a growing global concern due to their ubiquitous presence in water resources worldwide and their potential adverse effects on human health and ecosystems. Limited scientific understanding of sources of emerging pollutants’ emissions to water bodies and their pathways, behaviour, and fate in aquatic environments, as well as human health and ecological effects, is a significant hindrance in managing emerging water pollutants. With exceptions concerning PFAS/PFOS and microplastic beads, there are few regulations for emerging pollutants in national water and environmental policies, which results in a critical gap in safeguarding human health and aquatic ecosystems through effective prevention, reduction, and management strategies. This chapter presents a set of science-based policy recommendations for managing emerging water pollutants, particularly for the protection of aquatic ecosystems and groundwater resources, as well as through proper wastewater and waste management, including the circular economy approach and lifecycle management of pollutants. Policy recommendations are also proposed for managing priority emerging pollutants such as microplastics, nanomaterials, and trace chemicals. The policy recommendations emanate from key policy-relevant findings of research studies and scientific discussions presented at the UNESCO-IWRA International Conference on “Emerging Pollutants: Protecting Water Quality for the Health of People and Ecosystems,” which took place online in January 2023, gathering over 170 state-of-the-art research studies on wide-ranging topics related to emerging water pollutants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".